Automatic anomaly alerts for scheduled posts

ABSTRACT

Techniques are disclosed for automatically detecting anomalies in the content of a scheduled social media post, alerting a user to the presence of such anomalies before the content is posted and recommending a course of action when an anomaly is detected. A set of keywords is extracted from a scheduled post using an ontological classification technique. At predetermined time intervals, the keywords are compared with information obtained from one or more data sources to determine if an anomaly is present. If an anomaly is detected, the scheduled post is classified into one of at least three categories: supporting the post, neutral, or opposing the post. Once the anomaly is detected and the scheduled post is classified, the author of the post is alerted to the anomaly along with the categorization. Subsequently, the author may reschedule the post to an earlier or later time, delete the post, or change the post.

FIELD OF THE DISCLOSURE

This disclosure relates to the field of data processing, and moreparticularly, to techniques for automatically detecting anomalies in thecontent of a scheduled social media post, alerting a user to thepresence of such anomalies before the content is posted, andrecommending a course of action when an anomaly is detected.

BACKGROUND

Social networking, websites and other services allow individuals tointeract with other people via the Internet and other electroniccommunication channels. For instance, social networking can be used bycompanies for marketing and customer service. Companies that utilizesocial networking often plan and schedule content to be posted to theirsocial media properties well in advance of the publication date. Thetime window between post creation and publication (posting) can rangefrom a few hours or days to a few weeks or more. During, this timewindow, external events that are relevant to the scheduled posts mayoccur unexpectedly and unpredictably. In many cases, the content authorwill wish to review the scheduled posts before they are published toensure that the posts are appropriate in light of these external events.Therefore, there is a need for techniques for automatically flaggingscheduled posts and bringing these events to the attention of thecontent author.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral.

FIG. 1 illustrates an example client-server system for automatic anomalyalerts for scheduled posts, in accordance with an embodiment of thepresent invention.

FIG. 2 is a data flow diagram representative of an example methodologyfor automatic anomaly alerts for scheduled posts, in accordance with anembodiment.

FIG. 3 illustrates an example graphical user interfaces for a webpage,in accordance an embodiment of the present invention.

FIG. 4 is a flow diagram of an example methodology for automatic anomalyalerts for scheduled posts, in accordance with an embodiment of thepresent invention.

FIG. 5 is a block diagram representing an example computing device thatmay be used in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION

Electronic content that has been created and scheduled for publication(e.g., via a website or social media channel) at a future date isreferred to as a scheduled post. External events can occur during theperiod between creation and publication of the scheduled post. Dependingon the nature of the external events, these events can have an effect,sometimes positive or negative, on the scheduled post. For example, amagazine publisher may schedule a post promoting an interview with afamous actor that will be published in an upcoming issue. A week beforethe content is scheduled to post, the actor receives a prestigious awardfor her humanitarian work in a developing country, which is reported byseveral online media outlets. In this situation, it may be desirable forthe publisher to modify the post before scheduled publication to reflectthis development and improve the timeliness and relevance of thecontent. In another example, a travel website may schedule a postadvertising an upcoming cruise in the Mediterranean. Two days before thecontent is scheduled to post, a cruise ship in the Caribbean is forcedto return to port after a fire occurs in the engine room, leading to asignificant amount of negative public sentiment toward the cruiseindustry across various social media platforms. In this situation, itmay be desirable for the travel website to postpone the post until alater time. However, there are no existing automated techniques forassociating these events with the content of a scheduled post andalerting the author before the post is published. Instead, only manualand ad-hoc solutions exist. As such, authors of social media contentmust manually keep track of external events that are relevant to allscheduled posts, which can be very difficult, particularly where thecontent spans multiple topics, geographical locations and time periods.Furthermore, in some instances such authors may not ever become aware ofsome events before the post is published, which can result in a missedmarketing opportunity or an undesirable response from the public,depending on the nature of the event, the content of the post, and thetiming of the post with respect to the occurrence of the event.

To this end, and in accordance with an embodiment of the presentinvention, techniques are disclosed for automatically detectinganomalies in the content of a scheduled social media post, alerting, auser to the presence of such anomalies before the content is posted andrecommending a course of action when an anomaly is detected. A set ofkeywords is extracted from a scheduled post using an ontologicalclassification technique. At predetermined time intervals, the keywordsare compared with information obtained from one or more data sources,such as social media platforms and web search engines, to determine ifan anomaly is present. An anomaly is declared or detected or otherwiseoccurs when a deviation exceeds an algorithmically-derived threshold.The deviation may include, for example, the number of instances akeyword is mentioned in the data sources, the geo-location of thementions, or any other parameter of interest. If an anomaly is detected,the scheduled post is classified into one of at least three categories:supporting the post, neutral, or opposing the post. The classificationis based on whether the sentiment and emotion of the external mentionsmatch those of the post. If the emotion and sentiment of the externalmentions are similar to the emotion and sentiment of the post, thescheduled post is classified as supporting. Likewise, if the emotion andsentiment of the external mentions are opposite from the emotion andsentiment of the post, the scheduled post is classified as opposing. Ifthe relation cannot be determined, the scheduled post is classified asneutral. Once the anomaly is detected and the scheduled post isclassified, the author of the post is alerted to the anomaly along, withthe categorization. Such alerts can be through email, push notificationsin a mobile application, text messaging, and any other type ofelectronic communication channel. Subsequently, the author mayreschedule the post to an earlier or later time, delete the post, orchange the post. Numerous configurations and variations will be apparentin light of this disclosure.

The term “content,” as used in this disclosure, generally refers to anytype of information that a user can interact with electronically,including, for example, text, images, audio, video, and graphics.Content may be included in documents, data, applications, services, webpages, e-mail messages, and electronic files. Examples of such contentinclude messages that are posted to a social networking website,messages that are sent from one user computing device to another via aninstant messaging or e-mail service, and photographs that are displayedon a blog. Content can, in some cases, include metadata (i.e., dataabout data) that may not be directly accessible by a user, such asmessage routing information, timestamps, authentication credentials,browser cookies, etc. Content can include natural language text thatcorresponds more closely with a human language, such as English, than anartificial language, such as C++. Other examples will be apparent inlight of the present disclosure.

The term “post,” as used in this disclosure, generally refers to anytype of content that is published electronically. Publication of a postmay occur, for example, when the content is made accessible to one ormore users via a website, electronic bulletin board, social mediaapplication, or other suitable application.

The term “anomaly,” as used in this disclosure, generally refers to acondition in which a deviation, inconsistency or incongruity from anexpected state occurs in a set of data, and in particular, a post. Forexample, the statement “the sun is shining here” is an anomaly when madebetween sunset and sunrise at a particular location. In another example,a news report today that a store has gone out of business is an anomalyif the store had previously announced a grand opening for next, week. Inyet another example, a business announcement that earnings haveincreased 25% is an anomaly if the projected earnings were previouslyannounced as 10%. Other such examples will be apparent in light of thisdisclosure.

Example System

FIG. 1 illustrates an example client-server system 100 for automaticallydetecting anomalies in the content of a scheduled social media post,alerting a user to the presence of such anomalies before the content isposted, and recommending a course of action when an anomaly is detected,in accordance with an embodiment. The system 100 includes one or moreuser computing devices 110 a, 110 b, 110 c and a server 120, eachelectronically interconnected via a network 140 (e.g., a wide areanetwork, such as the Internet, or a local area network). Generally, thecomputing devices 110 a, 110 b, 110 c can be any type of device, such asa PC, tablet, or smart phone, configured to access and provide content(e.g., a web page, a word processing document, a fixed layout document,etc.) provisioned by the server 120 or another content-providing server.It will be understood that the functions of the computing devicesvariously described in this disclosure can be performed on and by anynumber of computing devices, depending on the particular application ofthe system. For instance, one or more of the computing devices 110 a,110 b, 110 c, the server 120, or any combination, of these can include acontent generation module 112, a post scheduling module 122, anontological classification engine 124, an anomaly detection module 126,a classification module 128, and an alert and recommendation module 130.The user computing devices idea, 110 a, 110 c can each include a browser114 or other application suitable for retrieving, processing, displayingand interacting with the content. Data sources representing data that isexternal to the system 100 can include social media services (Facebook®,Twitter®, Google+®, Pinterest®, Instagram®, LinkedIn®, YouTube®,Foursquare®, etc.), websites (abcnews.com, cbc.ca, nytimes.com,bbc.co.uk, cnn.com, huffingtonpost.com, espn.go.com, etc.), blogsnewsgroups, news feeds, or any other source of electronic dataaccessible via the network 140 or other suitable communication channel.

FIG. 2 is a data flow diagram 200 representative of an examplemethodology for automatically detecting anomalies in the content of ascheduled social media post, alerting a user to the presence of suchanomalies before the content is posted, and recommending a course ofaction when an anomaly is detected, in accordance with an embodiment.Such a methodology can be implemented in conjunction with the system 100of FIG. 1. In use, the content generation module 112 can be configuredto receive content generated by a user, such as a post. For instance, auser can use the content generation module 112 (e.g., via the browser114) to generate content 202 that is provided to the post schedulingmodule 122. The post scheduling module 122 schedules a time in thefuture at which the user-generated content 202 is to be published, andstores the scheduled post 204 in a scheduled post queue 250. Thescheduled post 204 includes information about the time the post isscheduled to be published as well as the content of the post and anyother tags, information or meta-data the user provides to associate thepost with a particular topic or subject matter.

In an embodiment, the ontological classification engine 124 extracts keytopics, concepts and keywords from the scheduled post 206 using anysuitable ontological classification algorithm. For example, theontological classification, engine 124 may be rule-based, and may befurther adapted for a particular class of topics (e.g., topics relatingto certain types of pop culture, politics, science, music, industry,etc.). The ontological classification engine 124 seeks to generate(e.g., using a corresponding set of rules) keywords, key phrases andtopics 208 that are semantically or logically relevant to the context ofthe scheduled post 206 by extracting terms from all or portions of thecontent and, in some cases, using other vocabulary obtained from priortraining or curation of the engine. For example, if the scheduled post206 includes the phrase “Japanese airline safety case study will bepresented by university researchers,” the ontological classificationengine 124 may generate keywords and topics 208 such as “airlinesafety,” “Japanese airline,” “commercial aviation,” “academic research,”and so forth. The ontological classification engine 124 may, in someinstances, also use the user-provided tags as a seed input to theclassification algorithm for algorithms that accept such seeds. Thekeywords and topics 208 can be grouped as a set specifically associatedwith the scheduled post 206. Other scheduled posts can be associatedwith different groups of keywords and topics.

In accordance with an embodiment of the present invention, the anomalydetection module 126 receives data 210 from an external data source 150,such as described with respect to FIG. 1. The data 210 may include anytype of information, such as social media posts, news articles, pressreleases, opinion pieces, advertisements, electronic books, academicpapers, government reports, and so forth. The anomaly detection module126 continuously or periodically receives and processes the data 210,which may change over time as new external information is generated inresponse to various events. In some embodiments, the age, or timeperiod, of the data 210 processed by the anomaly detection module 126 isconfigurable. For example, the anomaly detection module 126 may beconfigured to process external data 210 created prior to creation of thecontent 202 (e.g., news reports from one day, one week, two weeks, etc.,prior to creation of the post). This enables the anomaly detectionmodule 126 to reach back in time to process information that existedbefore the user created the post, if desired. Alternatively, the anomalydetection module 126 may be configured to process only external datacreated after the content 202 was created or during a fixed time periodprior to the scheduled publication of the post.

For a given set of keywords and topics 208 associated with the scheduledpost 206, the anomaly detection module 126 computes a deviation based onthe data 210 at one or more intervals of time (e.g., every 15 or 30minutes, every 1, 3, 6, 12, 24 hours, etc.) using an anomaly detectionalgorithm. Examples of such anomaly detection algorithms include neuralnetworks, Bayesian networks, support vector machines, rule-basedalgorithms. In one embodiment, the anomaly detection module 126 computesthe deviation by extracting keywords from the data 210 using the samealgorithm used to extract keywords for scheduled post. These extractedkeywords can be termed as feature vectors. One-to-one mapping can thenbe done for feature vectors to determine how many feature vectors forscheduled post matches that of data 210. If the match meets a thresholdthen it is an anomaly. The time intervals can be user-specified orautomatically determined based on the frequency or amount of new data210 (e.g., data not already processed) received over some period oftime. If the deviation exceeds a predetermined threshold value, ananomaly 212 is generated; otherwise, no anomaly is generated. Thedeviation can be based on any number of factors, such as the number oftimes any of the keywords is mentioned in the data 210, the geo-locationof the mentions or any other parameter of interest. For example, theanomaly 212 may be generated if one of the keywords 208 is mentioned inthe data 210 more than 100 times at locations within the United States.

In an embodiment, the classification module 128 classifies the anomaly212 into one of at least three categories: supporting, the post,neutral, or opposing the post. The classification is based on theemotion and sentiment of all the mentions around the set of keywords andtopics 208 associated with the scheduled post 206. Any sentiment enginecan be used. The sentiments can be based on mentions, where the mentionscan include the entire article, a keyword, comments received by thearticle, references to the article, or any other content that refers tothe keywords and topics 208. If the emotion and sentiment of thementions are similar to the emotion and sentiment of the scheduled post206, the anomaly 212 is classified as supporting the post. If theemotion and sentiment of the mentions are opposite the emotion andsentiment of the scheduled post 206, the anomaly 212 is classified asopposing the post. If the relation cannot be determined, the anomaly 212is classified as neutral. Further analysis is also possible. Forexample, the keywords that resulted in the anomaly being classified asopposing or supporting the post can be highlighted so that the user canfocus on such opposing keywords. The result of the classification is aclassified anomaly 214. Once an anomaly 212 is detected and classified,the user is alerted to the classified anomaly 214 by the alert andrecommendation module 130 via an alert or action 216 sent to the browser114 or other suitable user interface, such as email, push notificationsin a mobile application, or text messaging. In response to the alert216, the user can then take one of the following actions: delay thescheduled post 206 and change its scheduled time in the scheduled postqueue 205 to another time in the future; pull in the scheduled post 206and change its scheduled time in the scheduled post queue 250 to anothertime in the future; or suspend the scheduled post 206 and remove it fromthe scheduled post queue 250. In some cases, the alert andrecommendation module 130 can suggest to the user which of these actionsto take based on the classification, and the user can accept therecommended action or perform a different action at the user'sdiscretion. For example, if the classified anomaly 214 is supporting,then the recommended action may include pulling in the scheduled post206 to an earlier scheduled time. In another example, if the classifiedanomaly 214 is opposing, then the recommended action may includedelaying the scheduled post 206 to a later scheduled time.

Example User Interface

FIG. 3 is an example graphical user interface 300 configured for use inconjunction with the system 100 of FIG. 1, in accordance with anembodiment. The interface 300 may, for example, be implemented in thebrowser 114. The interface 300 is configured to display a scheduled post302, and alert 304, and a recommended action 306. The user can read thescheduled post 302 and the alert 304, and determine whether to take oneof the recommended actions 306 using, for example, a mouse to select thedesired action. In some cases, the alert 304 can contain a hyperlinkthat, when selected by the user, provides the user with more informationabout the external data that was used to generate the anomaly (e.g., thenews article, social media message, etc.).

Example Methodologies

FIG. 4 is a flow diagram of an example methodology 400 for automaticallydetecting anomalies in the content of a scheduled social media post,alerting a user to the presence of such anomalies before the content isposted, and recommending a course of action when an anomaly is detected.The example methodology 400 may, for example, be implemented in theserver 120 of FIG. 1. The method 400 begins by receiving 402 contentgenerated by a user and a scheduled posting time associated with thecontent. The method 400 continues by storing 404 the content into ascheduled post queue as a scheduled post. Unless the scheduled post ismodified or deleted, the content will automatically be published at oraround the scheduled posting, time without further user intervention.The method 400 continues by performing 406 an ontological classificationof the scheduled post to produce a set of keywords and topics associatedwith the scheduled post. As discussed with respect to FIG. 2, anysuitable ontological classification algorithm can be used, including butnot limited to neural networks, Bayesian networks, support vectormachines, and other rule-based algorithms. The method 400 continues byreceiving 408 data from an external data source. For example, alistening engine may search for and process publicly availableinformation on the Internet (e.g., newsfeeds social media postings, textmessages, and so forth). Using the external data and the set of keywordsand topics, the method 400 continues by detecting 410 an anomaly in thescheduled post. The anomaly can be any deviation, inconsistency orincongruity between the set of keywords and topics associated with thescheduled post and the external data. If an anomaly is detected, themethod 400 continues by classifying 412 the anomaly into one of threeclasses: supporting the post, neutral, or opposing the post. Theclassification can be based on the emotion and sentiment of the externaldata (e.g., positive, negative, powerful, weak, offensive, defensive,supportive, critical, pleased, displeased, aggressive, timid, and soforth). Finally, the method 400 continues by generating 414 an alertbased on the classified anomaly. The alert is configured to be presentedto the user via a graphical user interface, such as provided by a webbrowser or other suitable application.

Example Computing Device

FIG. 5 is a block diagram representing an example computing device 1000that may be used to perform any of the techniques as variously describedherein. For example, the user computing devices 110 a, 110 b, 110 c, theserver 120, or any combination of these (such as described with respectto FIG. 1) may be implemented in the computing device 1000. Thecomputing, device 1000 may be any computer system, such as aworkstation, desktop computer, server, laptop, handheld computer, tabletcomputer (e.g., the iPad™ tablet computer), mobile computing orcommunication device (e.g., the iPhone™ mobile communication device, theAndroid™ mobile communication device, and the like), or other form ofcomputing or telecommunications device that is capable of communicationand that has sufficient processor power and memory capacity to performthe operations described herein. A distributed computational system maybe provided comprising a plurality of such computing devices.

The computing device 1000 includes one or more storage devices 1010and/or non-transitory computer-readable media 1020 having encodedthereon one or more computer-executable instructions or software forimplementing techniques as variously described herein. The storagedevices 1010 may include a computer system memory or random accessmemory, such as a durable disk storage (which may include any suitableoptical or magnetic durable storage device, e.g., RAM, ROM, Flash, USBdrive, or other semiconductor-based storage medium), a hard-drive,CD-ROM, or other computer readable media, for storing data andcomputer-readable instructions and/or software that implement variousembodiments as taught herein. The storage device 1010 may include othertypes of memory as well, or combinations thereof. The storage device1010 may be provided on the computing device 1000 or provided separatelyor remotely from the computing device 1000. The non-transitorycomputer-readable media. 1020 may include, but are not limited to, oneor more types of hardware memory, non-transitory tangible media (forexample, one or more magnetic storage disks, one or more optical disks,one or more USB flash drives), and the like. The non-transitorycomputer-readable media 1020 included in the computing device 1000 maystore computer-readable and computer-executable instructions or softwarefor implementing various embodiments. The computer-readable media 1020may be provided on the computing device 1000 or provided separately orremotely from the computing device 1000.

The computing device 1000 also includes at least one processor 1030 forexecuting computer-readable and computer-executable instructions orsoftware stored in the storage device 1010 and/or non-transitorycomputer-readable media 1020 and other programs for controlling systemhardware. Virtualization may be employed in the computing device 1000 sothat infrastructure and resources in the computing device 1000 may beshared dynamically. For example, a virtual machine may be provided tohandle a process running on multiple processors so that the processappears to be using only one computing resource rather than multiplecomputing resources. Multiple virtual machines may also be used with oneprocessor.

A user may interact with the computing device 1000 through an outputdevice 1040, such as a screen or monitor, which may display one or moreuser interfaces provided in accordance with some embodiments. The outputdevice 1040 may also display other aspects, elements and/or informationor data associated with some embodiments. The computing device 1000 mayinclude other I/O devices 1050 for receiving input from a user, forexample, a keyboard, a joystick, a game controller, a pointing device(e.g., a mouse, a user's finger interfacing, directly with a displaydevice, etc.), or any suitable user interface. The computing device 1000may include other suitable conventional I/O peripherals. The computingdevice 1000 can include and/or be operatively coupled to varioussuitable devices for performing one or more of the functions asvariously described herein. For instance, the computing device mayinclude a network interface 1060 for communicating with other devicesvia a network, such as the Internet.

The computing device 1000 may run any operating system, such as any ofthe versions of Microsoft® Windows® operating systems, the differentreleases of the Unix and Linux operating systems, any version of theMacOS® for Macintosh computers, any embedded operating system, anyreal-time operating, system, any open source operating system, anyproprietary operating system, any operating systems for mobile computingdevices, or any other operating system capable of running on thecomputing device 1000 and performing the operations described herein. Inan embodiment, the operating system may be run on one or more cloudmachine instances.

In other embodiments, the functional components/modules may beimplemented with hardware, such as gate level logic (e.g., FPGA) or apurpose-built semiconductor (e.g., ASIC). Still other embodiments may beimplemented with a microcontroller having a number of input/output portsfor receiving and outputting data, and a number of embedded routines forcarrying out the functionality described herein. In a more generalsense, any suitable combination of hardware, software, and firmware canbe used, as will be apparent.

As will be appreciated in light of this disclosure, the various modulesand components of the system shown in FIG. 1, such as the contentgeneration module 112, the post scheduling module 122, the ontologicalclassification engine 124, the anomaly detection module 126, theclassification module 128, and the alert and recommendation module 130,can be implemented in software, such as a set of instructions (e.g., C,C++, object-oriented JavaScript, Java, BASIC, etc.) encoded on anycomputer readable medium or computer program product (e.g., hard drive,server, disc, or other suitable non-transient memory or set ofmemories), that when executed by one or more processors, cause thevarious methodologies provided herein to be carried out it will beappreciated that, in some embodiments, various functions performed bythe user computing system, as described herein, can be performed bysimilar processors and/or databases in different configurations andarrangements, and that the depicted embodiments are not intended to belimiting. Various components of this example embodiment, including theuser computing device 110 a, 110 b, 110 c and the server 120 can beintegrated into, for example, one or more desktop or laptop computers,workstations, tablets, smartphones, game consoles, set-top boxes, orother such computing devices. Other componentry and modules typical of acomputing system, such as processors (e.g., central processing unit andco-processor, graphics processor, etc.), input devices (e.g., keyboard,mouse, touch pad, touch screen, etc.), and operating system, are notshown but will be readily apparent.

Numerous embodiments will be apparent in light of the presentdisclosure, and features described herein can be combined in any numberof configurations. One example embodiment provides a system including astorage having at least one memory, and one or more processors eachoperatively coupled to the storage. The one or more processors areconfigured to carry out a process including receiving electronic contentgenerated by a user and a scheduled posting time associated with thecontent; identifying one or more keywords or topics associated with thescheduled post, or a combination of keywords and topics; receiving datafrom an external data source; detecting an anomaly in the scheduled postbased on the data and the keywords and topics; classifying the anomalymm one of a supporting anomaly, an opposing anomaly, and a neutralanomaly; and generating an alert based on the classified anomaly Thealert may be configured to be presented to the user via a graphical userinterface. In some cases, the process includes storing the electroniccontent into a scheduled post queue as a scheduled post to publish atthe scheduled posting, time. In some cases, the process includesreceiving a user request to reschedule the scheduled post at a differentscheduled posting time, and, in response thereto, changing the scheduledpost to publish at the different scheduled posting time. In some cases,the process includes receiving a user request to modify the scheduledpost with modified electronic content, and, in response thereto,modifying the scheduled post to include the modified electronic content.In some cases, the process includes receiving a user request to deletethe scheduled post, and, in response thereto, deleting the scheduledpost from the scheduled post queue. In some cases, the process includesgenerating a recommended action based on the classification of theanomaly the recommended action configured to be presented to the uservia the graphical user interface. In some such cases, the recommendedaction is reschedule the scheduled post, modify the scheduled post, ordelete the scheduled post. In some cases, the alert is generated priorto the scheduled posting, time. Another embodiment provides anon-transient computer-readable medium or computer program producthaving, instructions encoded thereon that when executed by one or moreprocessors cause the processor to perform one or more of the functionsdefined in the present disclosure, such as the methodologies variouslydescribed in this paragraph. As previously discussed, in some cases,some or all of the functions variously described in this paragraph canbe performed in any order and at any time by one or more differentprocessors.

The foregoing description and drawings of various embodiments arepresented by way of example only. These examples are not intended to beexhaustive or to limit the invention to the precise forms disclosed.Alterations, modifications, and variations will be apparent in light ofthis disclosure and are intended to be within the scope of the inventionas set forth in the claims.

What is claimed is:
 1. A computer-implemented method comprising:receiving, by a processor and via a user interface, electronic contentgenerated by a user and a scheduled posting time associated with thecontent; storing, by the processor, the electronic content into ascheduled post queue as a scheduled post to publish at the scheduledposting time; identifying, by the processor, at least one of a keywordand topic associated with the scheduled post; receiving, by theprocessor, data from an external data source excluding the electroniccontent generated by the user; detecting, by the processor, an anomalyin the scheduled post based on the data from the external source and thekeyword or topic, the anomaly representing at least one of a deviation,inconsistency, and incongruity between the at least one keyword andtopic associated with the scheduled post and the data from the externaldata source, wherein the anomaly is detected when a number of instancesthat the at least one keyword and topic deviates from the scheduled postexceeds a predetermined threshold; classifying, by the processor, theanomaly into one of a supporting anomaly, an opposing anomaly, and aneutral anomaly based on additional information about the keyword ortopic derived from the external data source; generating, by theprocessor, an alert based on the classified anomaly, the alert includinga hyperlink to the external data source, the hyperlink when selectedcauses the user interface to display the additional information aboutthe keyword or topic associated with the scheduled post; and causing, bythe processor, the user interface to display the scheduled post and thealert including the hyperlink prior to publication of the scheduled postat the scheduled posting time.
 2. The method of claim 1, wherein theidentifying includes performing, by the processor, an ontologicalclassification of the scheduled post to identify the at least onekeyword and topic.
 3. The method of claim 1, further comprisingreceiving a user request to reschedule the scheduled post at a differentscheduled posting time, and, in response thereto, changing, by theprocessor, the scheduled post to publish at the different scheduledposting time.
 4. The method of claim 1, further comprising receiving auser request to modify the scheduled post with modified electroniccontent, and, in response thereto, modifying, by the processor, thescheduled post to include the modified electronic content.
 5. The methodof claim 1, further comprising receiving a user request to delete thescheduled post, and, in response thereto, deleting, by the processor,the scheduled post from the scheduled post queue.
 6. The method of claim1, further comprising generating, by the processor, a recommended actionbased on the classification of the anomaly, the recommended actionconfigured to be presented to the user via the graphical user interface.7. The method of claim 6, wherein the recommended action is one ofreschedule the scheduled post, modify the scheduled post, and delete thescheduled post.
 8. A system comprising: a storage; and a processoroperatively coupled to the storage and configured to execute instructionstored in the storage that when executed cause the processor to carryout a process comprising: receiving, via a user interface, electroniccontent generated by a user and a scheduled posting time associated withthe content; storing the electronic content into a scheduled post queueas a scheduled post to publish at the scheduled posting time; performingan ontological classification of the scheduled post to produce a set ofkeywords and topics associated with the scheduled post; receiving datafrom an external data source excluding the electronic content generatedby the user; detecting an anomaly in the scheduled post based on thedata from the external source and the set of keywords and topics, theanomaly representing at least one of a deviation, inconsistency, andincongruity between the at least one keyword and topic associated withthe scheduled post and the data from the external data source, whereinthe anomaly is detected when a number of instances that the at least onekeyword and topic deviates from the scheduled post exceeds apredetermined threshold; classifying the anomaly into one of asupporting anomaly, an opposing anomaly, and a neutral anomaly based onadditional information about the keyword or topic derived from theexternal data source; generating an alert based on the classifiedanomaly, the alert including a hyperlink to the external data source,the hyperlink when selected causes the user interface to display theadditional information about the keyword or topic associated with thescheduled post; and causing the user interface to display the scheduledpost and the alert including the hyperlink prior to publication of thescheduled post at the scheduled posting time.
 9. The system of claim 8,wherein the process includes receiving a user request to reschedule thescheduled post at a different scheduled posting time, and, in responsethereto, changing the scheduled post to publish at the differentscheduled posting time.
 10. The system of claim 8, wherein the processincludes receiving a user request to modify the scheduled post withmodified electronic content, and, in response thereto, modifying thescheduled post to include the modified electronic content.
 11. Thesystem of claim 8, wherein the process includes receiving a user requestto delete the scheduled post, and, in response thereto, deleting thescheduled post from the scheduled post queue.
 12. The system of claim 8,wherein the process includes generating a recommended action based onthe classification of the anomaly, the recommended action configured tobe presented to the user via the graphical user interface.
 13. Thesystem of claim 12, wherein the recommended action is one of reschedulethe scheduled post, modify the scheduled post, and delete the scheduledpost.
 14. The system of claim 8, wherein the alert is generated prior tothe scheduled posting time.
 15. A non-transitory computer readablemedium having instructions encoded thereon that when executed by one ormore processors cause a process to be carried out, the processcomprising: receiving, via a user interface, electronic contentgenerated by a user and a scheduled posting time associated with thecontent; storing the electronic content into a scheduled post queue as ascheduled post to publish at the scheduled posting time; identifying atleast one of a keyword and topic associated with the scheduled post;receiving data from an external data source excluding the electroniccontent generated by the user; detecting an anomaly in the scheduledpost based on the data from the external source and the keyword ortopic, the anomaly representing at least one of a deviation,inconsistency, and incongruity between the at least one keyword andtopic associated with the scheduled post and the data from the externaldata source, wherein the anomaly is detected when a number of instancesthat the at least one keyword and topic deviates from the scheduled postexceeds a predetermined threshold; classifying the anomaly into one of asupporting anomaly, an opposing anomaly, and a neutral anomaly based onadditional information about the keyword or topic derived from theexternal data source; generating an alert based on the classifiedanomaly, the alert including a hyperlink to the external data source,the hyperlink when selected causes the user interface to display theadditional information about the keyword or topic associated with thescheduled post; and causing the user interface to display the scheduledpost and the alert including the hyperlink prior to publication of thescheduled post at the scheduled posting time.
 16. The non-transitorycomputer readable medium of claim 15, wherein the process includesreceiving a user request to reschedule the scheduled post at a differentscheduled posting time, and, in response thereto, changing the scheduledpost to publish at the different scheduled posting time.
 17. Thenon-transitory computer readable medium of claim 15, wherein the processincludes receiving a user request to modify the scheduled post withmodified electronic content, and, in response thereto, modifying thescheduled post to include the modified electronic content.
 18. Thenon-transitory computer readable medium of claim 15, wherein the processincludes receiving a user request to delete the scheduled post, and, inresponse thereto, deleting the scheduled post from the scheduled postqueue.
 19. The non-transitory computer readable medium of claim 15,wherein the process includes generating a recommended action based onthe classification of the anomaly, the recommended action configured tobe presented to the user via the graphical user interface, wherein therecommended action is one of reschedule the scheduled post, modify thescheduled post, and delete the scheduled post.
 20. The non-transitorycomputer readable medium of claim 15, wherein the alert is generatedprior to the scheduled posting time.